Ontology-Driven Analytic Models for Pension Management and Decision Support System

Ontology-Driven Analytic Models for Pension Management are sophisticated approaches that integrate the principles of ontology and analytics to optimize the management and decision-making processes within pension systems. While Ontology-Driven Analytic Models offer significant benefits for pension management, there are also challenges associated with implementing and utilizing the models. Developing a comprehensive and accurate ontology for pension management requires a deep understanding of the domain, including regulatory frameworks, investment strategies, retirement planning, and integration of data from heterogenous sources. Integrating these data into a cohesive ontology can be challenging. This research work leverages on semantic ontology as an approach for structured representation of knowledge about concepts and their relationships, and applies it to analyze and optimize decision support for pension management. The proposed ontology presents a formal and explicit specification of concepts (classes), their attributes, and the relationships between them and provides a shared and standardized understanding of the domain;enabling precise communication and knowledge representation for decision-support. The ontology deploys computational frameworks and analytic models to assess and evaluate data, generate insights, predict future pension fund performance as well as assess risk exposure. The research adopts the Reasoner, SPARQL query and OWL Visualizer executed over Java IDE for modelling the ontology-driven analytics. The approach encapsulated and integrated semantic ontologies with analytical models to enhance the accuracy, contextuality, and comprehensiveness of analyses and decisions within pension systems.

Optimization of RDF link traversal based query execution

Aiming at the problem that only some types of SPARQL （ simple protocal and resource description framework query language） queries can be answered by using the current resource description framework link traversal based query execution （RDF-LTE） approach, this paper discusses how the execution order of the triple pattern affects the query results and cost based on concrete SPARQL queries, and analyzes two properties of the web of linked data, missing backward links and missing contingency solution. Then three heuristic principles for logic query plan optimization, namely, the filtered basic graph pattern （FBGP） principle, the triple pattern chain principle and the seed URIs principle, are proposed. The three principles contribute to decrease the intermediate solutions and increase the types of queries that can be answered. The effectiveness and feasibility of the proposed approach is evaluated. The experimental results show that more query results can be returned with less cost, thus enabling users to develop the full potential of the web of linked data.

Parallel algorithm of generating closure of RDFS source

To decrease the time of generating a closure, a parallel algorithm of generating the closure of a resource description framework schema （RDFS） source is presented. In the algorithm, RDFS triples in the source are classified according to the forms of triples in the entailment rules and it reduces the scope of searching for specific triples. The dependence among the classes of triples is analyzed. Based on the classification, the initial RDFS source is partitioned into several subsets. The subsets are distributed to each process, and the closure is generated in parallel by applying the RDFS entailment rules. Parallel generating the closure of an RDFS source takes less time and increases efficiency.

RDF(S) repository privacy protection via inference control

The privacy protection of resource description framework （schema） （RDF（S） ） repository is an emerging topic in database security area. In this paper, entailment rules are investigated based on RDF（S） repository firstly. Then, an idea that uses reasoning closure to judge whether the privacy disclosure caused by inference is existed is proposed. Furthermore, the definitions of impli- cation conditions and information measure of triple statements which gains data hiding algorithm with combining proposition logic reasoning theory are introduced. Meanwhile, a conversion method from conjunctive normal form to disjunctive normal form based minimal hitting sets of set cluster is aiso proposed. Finally, the experimental results show that our algorithm can prevent privacy disclosure of RDF（S） repository effectively.

Monitoring of Unaccounted for Gas in Energy Domain Using Semantic Web Technologies

Smart Urbanization has increased tremendously over the last few years,and this has exacerbated problems in all areas of life,especially in the energy sector.The Internet of Things(IoT)is providing effective solutions in gas distribution,transmission and billing through very sophisticated sensory devices and software.Billions of heterogeneous devices link to each other in smart urbanization,and this has led to the Semantic interoperability(SI)problem between the connected devices.In the energy field,such as electricity and gas,several devices are interlinked.These devices are competent for their specific operational role but unable to communicate across the operational units as required for accounting and monitoring of gas losses due to heterogeneity in device communication standards.To overcome this problem,we have proposed a model and ontology by applying semantic web technologies and cloud storage to address the tracking of customers to observe Unaccounted for gas(UFG)in the gas domain of energy.Semantization is achieved by replicating heterogeneous devices Sensor Model Language(SenML)data into Resource description framework(RDF)without human interventions.As semantic interoperability is used to efficiently and meaningfully share the information from one location to another.Therefore,the proposed ontology and model focus more efficiently on customer tracking,forecasting,and monitoring to detect UFG in gas networks.This also helps to save Gas Companies from financial gas losses.

Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain

Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.

Towards Efficient SPARQL Query Processing on RDF Data

Efficient support for querying large-scale resource description framework （RDF） triples plays an important role in semantic web data management. This paper presents an efficient RDF query engine to evaluate SPARQL queries, where the inverted index structure is employed for indexing the RDF triples. A set of operators on the inverted index was developed for query optimization and evaluation. Then a main-tree-shaped optimization algorithm was developed that transforms a SPARQL query graph into the op-timal query plan by effectively reducing the search space to determine the optimal joining order. The opti-mization collects a set of RDF statistics for estimating the execution cost of the query plan. Finally the opti-mal query plan is evaluated using the defined operators for answering the given SPARQL query. Extensive tests were conducted on both synthetic and real datasets containing up to 100 million triples to evaluate this approach with the results showing that this approach can answer most queries within 1 s and is extremely efficient and scalable in comparison with previous best state-of-the-art RDF stores.

Improving SPARQL query performance with algebraic expression tree based caching and entity caching

To obtain comparable high query performance with relational databases,diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework(RDF) data and SPARQL query.Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle.However,existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics.Although semantic caching approaches work effectively in this case,little work has been done in this area.In this paper,we try to improve SPARQL query performance with semantic caching approaches,i.e.,SPARQL algebraic expression tree(AET) based caching and entity caching.Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches.The approaches are implemented on a two-level-storage structure.The main memory stores the most frequently accessed cache items,and items swapped out are stored on the disk for future possible reuse.Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches.Comparisons with previous research are also provided.

Exposing vocabularies for soil as Linked Open Data

Standards to describe soil properties are well established,with many ISO specifications and a few international thesauri available for specific applications.Besides,in recent years,the European directive on "Infrastructure for Spatial Information in the European Community(INSPIRE)"has brought together most of the existing standards into a well defined model.However,the adoption of these standards so far has not reached the level of semantic interoperability,defined in the paper,which would facilitate the building of data services that reuse and combine data from different sources.This paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply Linked Data technologies to existing standards and data in order to improve the interoperability of soil datasets.The main result of this work is twofold.First,an RDF vocabulary for soil concepts based on the UML INSPIRE model was published.Second,a KOS(Knowledge Organization System)for soil data was published and mapped to existing relevant KOS,based on the analysis of the SISI database of the CREA of Italy.This work also has a methodological value,in that it proposes and applies a methodology to standardize metadata used in local scientific databases,a very common situation in the scientific domain.Finally,this work aims at contributing towards a wider adoption of the INSPIRE directive,by providing an RDF version of it.

An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples

Semantic Web has emerged to make web content machine-readable,and with the rapid increase in the number of web pages,its importance has increased.Resource description framework(RDF)is a special data graph format where Semantic Web data are stored and it can be queried by SPARQL query language.The challenge is to find the optimal query order that results in the shortest period of time.In this paper,the discrete Artificial Bee Colony(dABCSPARQL)algorithm is proposed,based on a novel heuristic approach,namely reordering SPARQL queries.The processing time of queries with different shapes and sizes is minimized using the dABCSPARQL algorithm.The performance of the proposed method is evaluated on chain,star,cyclic,and chain-star queries of different sizes from the Lehigh University Benchmark(LUBM)dataset.The results obtained by the proposed method are compared with those of ARQ(a SPARQL processor for Jena)query engine,the Ant System,the Elitist Ant System,and MAX-MIN Ant System algorithms.The experiments demonstrate that the proposed method significantly reduces the processing time,and in most queries,the reduction rate is higher compared with other optimization methods.